Analysis
ABB
11 July, 2023
Plan of analysis to be carried out
for simple diagrams,
for complex connections - sankey diagram - for the link between printing methods + printing model with a link to the bioink type (+ origin)?
first things first, to give numbers of studies that report e.g. A or B
Printing Techniques
Give an overview of the different printing techniques, - method - printer model - printer source - forms - software
Kind of Printer Method
t1 <- table(reconciled$`4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`)
##
# Extrusion based Extrusion based|Unclear Inject based
# 37 1 4
# Other Stereolithography Unclear
# 9 2 10
# proportions instead of absolute value of number of studies
prop.table(table(reconciled$`4.1 What kind of printing method is used?_ae684e28-4f90-44ad-9de2-731d076de0b0_Answer`))##
## Extrusion based Extrusion based|Unclear Inject based
## 0.58730159 0.01587302 0.06349206
## Other Stereolithography Unclear
## 0.14285714 0.03174603 0.15873016 # barplot(t1, ylab = "Number of studies", ylim=c(0, max(t1) + 20),cex.names=.5 , col = "green")
text(x = barplot(t1, ylab = "Number of studies", ylim=c(0, max(t1) + 10), cex.names=.5 , col = "green"), y = t1 + 2, labels = t1, cex = 0.8)
# -- link printing method with bioInk type - Reporting of Printer Model
#### Reporting of Printer Model
t2 <- table(reconciled$`4.1.1 Do the authors report the printer model name/number?_a3e72486-56d3-4154-a45f-9fb87b8612fc_Answer`)
# Not reported Not reported|Reported Reported
# 22 1 4
# barplot(table(reconciled$`4.1.1 Do the authors report the printer model name/number?_a3e72486-56d3-4154-a45f-9fb87b8612fc_Answer`), ylab = "Number of studies")
text(x = barplot(t2, ylab = "Number of studies", ylim=c(0, max(t2) + 10), cex.names=.5 , col = "green"), y = t2 + 2, labels = t2, cex = 0.8)
Printer Source
#### Reporting of Printer Source
t3 <- table(reconciled$`4.1.2 What is the source of the printer?_102ba965-5234-4b3e-8b23-bd70cf4e074d_Answer`)
# commercial modified commercial not reported
# 36 4 4
# self-made unclear unclear|commercial
# 13 5 1
# barplot(table(reconciled$`4.1.2 What is the source of the printer?_102ba965-5234-4b3e-8b23-bd70cf4e074d_Answer`), ylab = "Number of studies", cex.names=.5, col = "green")
text(x = barplot(t3, ylab = "Number of studies", ylim=c(0, max(t3) + 10), cex.names=.5 , col = "green"), y = t3 + 2, labels = t3, cex = 0.8)
Printer Forms
#### Reporting of Printer forms
# split is based on the ink
table(reconciled$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`)##
## Grid Grid;Other
## 19 1
## Grid|Grid lobular liver
## 2 7
## lobular liver;Other lobular liver|lobular liver
## 1 2
## Other Other|Other
## 25 5
## Toroids
## 1 # Grid Grid;Other
# 19 1
# Grid|Grid lobular liver
# 2 7
# lobular liver;Other lobular liver|lobular liver
# 1 2
# Other Other|Other
# 25 5
# Toroids
# 1
# ABB to clean these responses
# Those with a semicolon response ; should now be called "combo"
# responses separate with a pipe should be merged when they are saying the same thing.
printer_forms <- reconciled %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Grid|Grid" = "Grid")) %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "lobular liver|lobular liver" = "lobular liver")) %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Other|Other" = "Other")) %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "Grid;Other" = "Combination")) %>%
mutate(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`= recode(`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, "lobular liver;Other" = "Combination"))
t4 <-table(printer_forms$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`)
# barplot(table(printer_forms$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`), ylab = "Number of studies", cex.names=.5 , col = "green")
text(x = barplot(t4, ylab = "Number of studies", ylim=c(0, max(t4) + 10), cex.names=.5 , col = "green"), y = t4 + 2, labels = t4, cex = 0.8)
Printer Forms Overtime
printer_form_year<- table(printer_forms$`4.1.4 What kind of forms are printed with this ink?_01d3ca37-1e2f-4087-8f39-c183f98f0c4e_Answer`, printer_forms$NA_Year)
printer_form_yearDF <- as.data.frame(printer_form_year)
# long to wide
printer_form_yearDF <- printer_form_yearDF %>% spread(
key = Var2,
value = Freq)
# printer_form_yearDF
# names(printer_form_yearDF) <- gsub(x = names(printer_form_yearDF), pattern = "X", replacement = "")
library(formattable)
formattable(printer_form_yearDF,
#align =c("l", "r"),
list(
`Indicator Name` = formatter(
"span",
style = ~ style(color = "grey",font.weight = "bold")),
area(row = 1:5) ~ color_tile("white", "green")))## Warning in gradient(as.numeric(x), ...): NAs introduced by coercion| Var1 | 2010 | 2011 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Grid | 0 | 0 | 0 | 1 | 0 | 2 | 4 | 4 | 1 | 4 | 5 | 0 |
| Combination | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
| lobular liver | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 1 | 2 | 2 | 1 |
| Other | 1 | 1 | 1 | 0 | 1 | 5 | 2 | 6 | 3 | 5 | 2 | 3 |
| Toroids | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Printer Software
#### Reporting of Printer software
table(reconciled$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`)##
## No No|No Not applicable Yes
## 39 1 1 22 # No No|No Not applicable Yes
# 39 1 1 22
## ABB combine No|No into the "No" group
printer_soft <- reconciled %>%
mutate(`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer` = recode(`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`, "No|No" = "No") )
t5 <- table(printer_soft$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`)
# barplot(table(printer_soft$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`), ylab = "Number of studies", cex.names=.5, col = "green")
text(x = barplot(t5, ylab = "Number of studies", ylim=c(0, max(t5) + 10), cex.names=.5 , col = "green"), y = t5 + 2, labels = t5, cex = 0.8)
## Slicing Software
# - since the review started, many cases - not applicable
# merge NA with No and merge No|No with NO
table(reconciled$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`)##
## No No|No Not applicable Yes
## 53 1 1 8 # No No|No Not applicable Yes
# 53 1 1 8
## ABB to clean No|No and Not applicable into the No option.
printer_slice <- printer_soft %>%
mutate(`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer` = recode(`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`, "No|No" = "No")) %>%
mutate(`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer` = recode(`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`, "Not applicable" = "No"))
t6 <- table(printer_slice$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`)
# t6_6 <- table(printer_slice$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`, printer_slice$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`)
# t6_6
# barplot(table(printer_slice$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`), ylab = "Number of studies", cex.names=.5, col = "green")
text(x = barplot(t6, ylab = "Number of studies", ylim=c(0, max(t6) + 10), cex.names=.5 , col = "green", main = "Is the name of the slicing software reported?"), y = t6 + 2, labels = t6, cex = 0.8, )
#####
# stacked barplot to combine software & slice software
# library(ggplot2)
#
# software <- printer_slice %>%
# group_by(`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`, printer_slice$`4.1.6 Do the authors report the name of slicing software?_bc07fc45-10aa-441f-9bdf-b32f5037389f_Answer`) %>% count()
#
# colnames(software) <- c("threeD-software", "slice-software", "number-studies")
#
# #, printer_slice$`4.1.5 Do the authors report the name of the 3D modelling software?_f88d82a9-410c-4118-ad6a-4535d12c4aca_Answer`))
#
# ggplot(software, aes(fill=`slice-software`, y=`number-studies`, x=`threeD-software`)) +
# geom_bar(position='stack', stat='identity')
#software$`number-studies`
############BioInks
Give an overview of the different inks used with these techniques. - type - origin - additives - density
ABB link bioink to printing method
## Type of BioInk
table(reconciled$`4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`)##
## Natural Natural|Natural Natural|Synthetic
## 43 3 5
## Not reported Not reported|Natural Synthetic
## 2 1 4
## Unclear
## 5# clean?
# Natural Natural|Natural Natural|Synthetic
# 43 3 5
# Not reported Not reported|Natural Synthetic
# 2 1 4
# Unclear
# 5
barplot(table(reconciled$`4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`), ylab = "Number of studies", cex.names=.5, col = "blue")
## If natural - what is the type type
table(reconciled$`4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`)##
## Other
## 1
## Polysaccharide based
## 10
## Polysaccharide based;dECM based
## 1
## Protein based
## 14
## Protein based;dECM based
## 8
## Protein based;Polysaccharide based
## 13
## Protein based;Polysaccharide based;dECM based
## 1
## Protein based;Polysaccharide based;Other
## 1
## Protein based;Polysaccharide based|Protein based;Polysaccharide based
## 1
## Protein based|dECM based
## 1
## Protein based|Protein based
## 1# output needs cleaning
table(reconciled$`4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`)##
## Other Pluronic
## 3 3
## Poly ethylene glycol (PEG)
## 3## 3rd level
table(reconciled$`4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`)##
## Collagens Collagens;Gelatin Collagens|Collagens
## 7 3 1
## Fibrinogen Fibrinogen;Other Gelatin
## 1 1 21
## Gelatin;Fibrinogen Gelatin;Other Gelatin;Silk-fibroin
## 1 2 1
## Gelatin|Gelatin Other
## 1 1table(reconciled$`4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`) ##
## Agarose Alginates
## 1 19
## Alginates;Other Chitosan
## 4 1
## Hyaluronic acid Hyaluronic acid|Hyaluronic acid
## 1 1## Origin of BioInk
table(reconciled$`4.1.3.2. What is the origin of the bioink?_34ef46b6-34ac-4ddb-a5de-07bc74272fca_Answer`)##
## commercial (ready-to-use) custom formulated
## 6 44
## custom formulated|custom formulated Not reported
## 8 2
## Unclear Unclear|Unclear
## 2 1# needs cleaning
# commercial (ready-to-use) custom formulated
# 6 44
# custom formulated|custom formulated Not reported
# 8 2
# Unclear Unclear|Unclear
# 2 1
barplot(table(reconciled$`4.1.3.2. What is the origin of the bioink?_34ef46b6-34ac-4ddb-a5de-07bc74272fca_Answer`), ylab = "Number of studies", cex.names=.5, col = "blue")
### Additives
# -- needs cleaning
table(reconciled$`4.1.3.3 Which information is provided on the additives in the ink or culture? _4c2b5908-9c3c-4ba8-91f2-dd9771ac2ad4_Answer`)##
## Concentration
## 9
## Concentration;Manufacturer
## 25
## Concentration;Manufacturer;Order number
## 8
## Concentration;Manufacturer;Order number|Concentration;Manufacturer;Order number
## 2
## Concentration;Manufacturer|Concentration;Manufacturer
## 4
## Concentration|Concentration;Manufacturer
## 1
## Manufacturer
## 1
## None
## 11
## None|Concentration;Manufacturer
## 1
## None|None
## 1# needs cleaning
## Cell density of bioInk
table(reconciled$`4.1.3.4 Is the cell density of the bioink provided in the study? _d03a497a-c8ab-49a6-8fa7-5eca0d168c94_Answer`)##
## No No|No No|Yes Yes Yes|No Yes|Yes
## 11 1 1 43 1 6# needs cleaning
# No No|No No|Yes Yes Yes|No Yes|Yes
# 11 1 1 43 1 6 BioInk Sunburst plot
#### BIOINK sunburst
# type -->
bioink <- reconciled %>%select(
study_ID,
`4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`,
`4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`,
`4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`, `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`,
`4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)
bioink <- bioink %>% rename(typeGeneral_level1 = `4.1.3 What type of bioink was used?_92421414-c597-4e9c-b720-9bb4318b5483_Answer`,
typeNatural_level2 = `4.1.3.1.1 If natural bioink, please choose the type._66b761ad-6630-4323-b423-c848a717aae4_Answer`,
typeSynthetic_level2 = `4.1.3.1.4 If Synthetic bioink: choose the type _e74ea0e0-a654-4ae2-b59e-2e0b14a4651a_Answer`,
typeProtein_level3 = `4.1.3.1.2 If Protein based: choose the type._621671b3-9f7e-4a22-80ac-14b02fdd0683_Answer`,
typePoly_level3 = `4.1.3.1.3 If Polysaccharides Based: choose the type. _fcea8bcb-da6c-4154-aa44-8ff4734fa4fe_Answer`
)
bioink_split1 <- separate_rows(bioink, typeGeneral_level1 , sep="\\|")
bioink_split1_1 <- separate_rows(bioink_split1, typeNatural_level2 , sep="\\|")
bioink_split1_2 <- separate_rows(bioink_split1_1, typeProtein_level3 , sep="\\|")
bioink_split1_3 <- separate_rows(bioink_split1_2, typePoly_level3 , sep="\\|")
bioink_split2 <- separate_rows(bioink_split1_2, typeNatural_level2 , sep=";")
bioink_split3 <- separate_rows(bioink_split2, typeProtein_level3 , sep=";")
bioink_split3 <- separate_rows(bioink_split3, typePoly_level3 , sep=";")
# 148 rows
test_bio <- bioink_split3 %>%
mutate(typeNatural_level2 = replace(typeNatural_level2, typeGeneral_level1!="Natural", NA),
typeSynthetic_level2 = replace(typeSynthetic_level2, typeGeneral_level1!="Synthetic", NA),
typeProtein_level3 = replace(typeProtein_level3, typeNatural_level2!="Protein based", NA),
typePoly_level3 = replace(typePoly_level3, typeNatural_level2!="Polysaccharide based", NA)
)
test_bio$typeSynthetic_level2 <- as.character(test_bio$typeSynthetic_level2)
### merge level 2 & merge level 3
test_bio$level3 <- ifelse(!is.na(test_bio$typeProtein_level3), test_bio$typeProtein_level3, test_bio$typePoly_level3)
test_bio$level2 <- ifelse(!is.na(test_bio$typeNatural_level2), test_bio$typeNatural_level2, test_bio$typeSynthetic_level2)
#### START PLOTTING
library(dplyr)
library(plotme)
bioInk_count <- count(test_bio,
typeGeneral_level1,
level2,
level3,
study_ID)
# sunburst plot
count_to_sunburst(bioInk_count)# fill by group size
count_to_sunburst(bioInk_count, fill_by_n = TRUE)# treemap plot, ordered by group size
count_to_treemap(bioInk_count, sort_by_n = TRUE)Liver Cells
And then it is liver specific variables. - main type of liver cells - info about liver cells
AND - Main type of liver cells
if comments from reconciled - print out for all to see.
if any of reviewer comments say HepG2
table(reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`)##
## Hepatoma cells Hepatoma cells;Other
## 34 2
## Induced pluripotent stem cells Induced pluripotent stem cells;Other
## 3 1
## Other Primary cells
## 7 14
## Primary cells;Hepatoma cells Primary cells;Other
## 1 1# clean? - Maren to clean
# Hepatoma cells Hepatoma cells;Other
# 28 2
# Induced pluripotent stem cells Induced pluripotent stem cells;Other
# 3 1
# Other Primary cells
# 12 14
# Primary cells;Hepatoma cells Primary cells;Other
# 2 1
### combine the ones with semi-colon - into a new category called "Combination"
barplot(table(reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`), ylab = "Number of studies", cex.names=.5, col = "turquoise")
# ABB bring in the "other" from comments box
table(reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Comments`)##
## AML12 hepatic parenchymal cells (murine)
## 1
## bone marrow mesenchymal cells
## 1
## cells isolated from cholangiocarinoma
## 1
## cryopreserved primary human hepatocytes
## 1
## from healthy liver biopsies
## 1
## Heb3B
## 1
## Hep3B
## 1
## Hep3B\n
## 1
## HepaRG
## 3
## HepaRG cells, LX-2 (hepatic stellate cell line)
## 1
## hepatocyte-like cells differentiated from adipose-derived mesenchymal stem cells
## 1
## hepatocyte-like cells directly converted from murine embryonic fibroblasts
## 1
## Hepatoma cells: derived from collagenase digestion of human HCC samples\n
## 1
## HepG2
## 18
## HepG2 C3A
## 1
## HepG2\n
## 1
## HepG2/C3A
## 4
## HepG2; human bone marrow-derived mesenchymal stem cells (BMMSCs)
## 1
## hiHep cells\n
## 1
## HMCS1SA
## 1
## Huh-7
## 1
## Huh-7 and HepaRG
## 1
## human adipose-derived stem cells (hASCs) differentiated towards hepatocyte-like cells (AHLCs)\n
## 1
## human adipose-derived stem cells were differntiated into hepatocytes
## 1
## Human induced pluripotent stem (hiPS) cell lines\nRCi-22 and RCi-50 and hESC lines RC-6 and RC-10, hESC-HLCs were printed.
## 1
## Human iPSC-derived hepatocytes
## 1
## Mouse primary hepatocyte
## 1
## Organoids from biopsies; immortalized cell line - HepG2
## 1
## primary cell liver spheroids
## 1
## primary cryopreserved human hepatocytes
## 1
## primary hepatocytes from murine livers
## 1
## primary human hepatocytes, other cells from liver: hepatic stellate cells; (HUVECs)
## 1
## primary human hepatocytes; human hepatic stellate cell line (LX2); primary fetal activated hepatic stellate cells (aHSC)\n
## 1
## primary mouse hepatocytes
## 1
## primary rat hepatocytes\n
## 1## clean the type of liver cells comments
liverType <- tibble(
study_ID = reconciled$study_ID,
liverCells = reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Answer`,
liverCellsComment = reconciled$`2.2 What is the main type of liver cells included?_9bea404f-a75c-401f-af5b-8fd020306538_Comments`
)
# remove line breaks
library(stringr)
liverType$liverCellsComment <- str_replace_all(liverType$liverCellsComment, "[\n]" , "")
## group the mouse ones together
# create categories
liverType$liverCellsComment <- as.factor(liverType$liverCellsComment)
commentsLiver <- liverType %>% group_by(liverCellsComment) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
#install.packages("formattable")
library(formattable)
formattable(commentsLiver,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("turquoise")
))| liverCellsComment | n_unique |
|---|---|
| HepG2 | 19 |
| NA | 6 |
| HepG2/C3A | 4 |
| HepaRG | 3 |
| Hep3B | 2 |
| AML12 hepatic parenchymal cells (murine) | 1 |
| bone marrow mesenchymal cells | 1 |
| cells isolated from cholangiocarinoma | 1 |
| cryopreserved primary human hepatocytes | 1 |
| from healthy liver biopsies | 1 |
| Heb3B | 1 |
| HepaRG cells, LX-2 (hepatic stellate cell line) | 1 |
| hepatocyte-like cells differentiated from adipose-derived mesenchymal stem cells | 1 |
| hepatocyte-like cells directly converted from murine embryonic fibroblasts | 1 |
| Hepatoma cells: derived from collagenase digestion of human HCC samples | 1 |
| HepG2 C3A | 1 |
| HepG2; human bone marrow-derived mesenchymal stem cells (BMMSCs) | 1 |
| hiHep cells | 1 |
| HMCS1SA | 1 |
| Huh-7 | 1 |
| Huh-7 and HepaRG | 1 |
| human adipose-derived stem cells (hASCs) differentiated towards hepatocyte-like cells (AHLCs) | 1 |
| human adipose-derived stem cells were differntiated into hepatocytes | 1 |
| Human induced pluripotent stem (hiPS) cell linesRCi-22 and RCi-50 and hESC lines RC-6 and RC-10, hESC-HLCs were printed. | 1 |
| Human iPSC-derived hepatocytes | 1 |
| Mouse primary hepatocyte | 1 |
| Organoids from biopsies; immortalized cell line - HepG2 | 1 |
| primary cell liver spheroids | 1 |
| primary cryopreserved human hepatocytes | 1 |
| primary hepatocytes from murine livers | 1 |
| primary human hepatocytes, other cells from liver: hepatic stellate cells; (HUVECs) | 1 |
| primary human hepatocytes; human hepatic stellate cell line (LX2); primary fetal activated hepatic stellate cells (aHSC) | 1 |
| primary mouse hepatocytes | 1 |
| primary rat hepatocytes | 1 |
#### how is LIVER model Cultered?
table(reconciled$`2.2.1 How is the presented liver model cultured?_b6e561ab-c358-47fa-b9db-61e363b73223_Answer`)##
## Co-culture Monoculture Unclear
## 33 29 1 # Co-culture Monoculture Unclear
# 33 29 1
barplot(table(reconciled$`2.2.1 How is the presented liver model cultured?_b6e561ab-c358-47fa-b9db-61e363b73223_Answer`), ylab = "Number of studies", cex.names=.7, col = "turquoise")
# If co-culter - what type of non-parenchymal cells
# - need to clean
table(reconciled$`2.2.1.1 If co-cultured, what type of non-parenchymal cells are included?_b729a62f-2dc7-47b6-ada7-e1ed8e363918_Answer`)##
## Endothelial cells
## 7
## Endothelial cells;Hepatic stellate cells
## 5
## Endothelial cells;Other
## 6
## Hepatic stellate cells
## 2
## Immune cells;Endothelial cells;Hepatic stellate cells
## 1
## Immune cells;Hepatic stellate cells
## 2
## Other
## 10# Maren to clean manually - bring in "other" responses from comments box
# LIVER Model
table(reconciled$`3.1 Does the study present a vascularization of the model?_3b31a6ff-4233-4f24-9e7e-2d2eb3c83420_Answer`)##
## No Yes, with perfusion Yes, without perfusion
## 52 5 6 # No Yes, with perfusion Yes, without perfusion
# 52 5 6
barplot(table(reconciled$`3.1 Does the study present a vascularization of the model?_3b31a6ff-4233-4f24-9e7e-2d2eb3c83420_Answer`), ylab = "Number of studies", cex.names=.7, col = "turquoise")
## hypoxia
table(reconciled$`3.2 Do the authors address hypoxia/normoxia/oxygenation of the liver model?_5b050ea7-da29-4a47-bdea-23edda5877f2_Answer`)##
## No Yes, by measurement Yes, descriptive
## 59 1 3 # No Yes, by measurement Yes, descriptive
# 59 1 3
barplot(table(reconciled$`3.2 Do the authors address hypoxia/normoxia/oxygenation of the liver model?_5b050ea7-da29-4a47-bdea-23edda5877f2_Answer`), ylab = "Number of studies", cex.names=.7, col = "turquoise")
Liver Cell Meta-Data
# Meta-data for Liver cells
table(reconciled$`2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer`)##
## Age Common cell line Common cell line;None
## 2 33 4
## Health status None Sex;Age
## 1 16 2
## Sex;Age;Common cell line Sex;Age;Health status
## 2 3## common cell line; None should get merged with Common Cell line option
# re-shape data to can number of bits of info?
liverCell_meta <- reconciled %>% mutate(`2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer` = recode(`2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer`, "Common cell line;None" = "Common cell line"))
liverMetaData <- separate_rows(liverCell_meta, `2.4 Which kind of meta data is available for the used liver cells?_2868ee50-2c4c-47e8-a040-427e4f882632_Answer` ,sep=";")
liverMetaData_heat <- liverMetaData[,c(1, 58)]
colnames(liverMetaData_heat) <- c("study_ID", "metaData")
countsMetaDat <- liverMetaData_heat %>% group_by(study_ID) %>% summarize(n_unique = length(unique(metaData)))
table(countsMetaDat$n_unique)##
## 1 2 3
## 56 2 5# 1 2 3
# 56 2 5
hist(countsMetaDat$n_unique, xlab = "Number of Meta-Data Items Reported", ylab = "Number of Papers", main = "Meta-Data Items about the Cell Lines Peformed per Paper", breaks = 3)
Cells
What types of included cells? - human, animal, both
table(reconciled$`2.1 What is the origin of the cells in the liver model?_d2a55b1d-f869-4fa4-83a1-94eeb126c6fb_Answer`)##
## Animal Both Human
## 4 14 45#
# Animal Both Human
# 4 14 45
barplot(table(reconciled$`2.1 What is the origin of the cells in the liver model?_d2a55b1d-f869-4fa4-83a1-94eeb126c6fb_Answer`), ylab = "Number of studies", cex.names=.7, col = "pink")
If human, is it Xeno-free?
table(reconciled$`2.1.1 If human, is the described liver model xeno-free/animal-free?_7b2396a2-218d-462b-b71f-0c68bc5e9911_Answer`)##
## No Unclear
## 43 2barplot(table(reconciled$`2.1.1 If human, is the described liver model xeno-free/animal-free?_7b2396a2-218d-462b-b71f-0c68bc5e9911_Answer`)
, ylab = "Number of studies", cex.names=.7, col = "pink")
Culture Conditions
What type of culture conditions? Liver Markers- what measurements?
table(reconciled$`5.2 How long were the liver models cultured after printing?_1f136f0a-786b-4e17-a97d-e5cf32f47b26_Answer`)##
## < 72 hours 2 weeks - 3 months 3 days - 2 weeks not reported
## 2 16 42 3 # < 72 hours 2 weeks - 3 months 3 days - 2 weeks not reported
# 2 16 42 3
barplot(table(reconciled$`5.2 How long were the liver models cultured after printing?_1f136f0a-786b-4e17-a97d-e5cf32f47b26_Answer`), ylab = "Number of studies", cex.names=.5, col = "purple")
## ABB to order these in time order Liver Markers
# WHICH
t <- table(reconciled$`6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Answer`)
# clean?
liverMarkers <- separate_rows(reconciled, `6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Answer` ,sep=";")
markers_heat <- liverMarkers[,c(1, 94)]
colnames(markers_heat) <- c("study_ID", "marker")
new_marker <- markers_heat %>% group_by(marker) %>% summarize(n_unique = length(unique(study_ID)))
new_marker$n_unique <- as.numeric(new_marker$n_unique)
sort_new_marker <- new_marker %>% arrange(desc(n_unique))
#install.packages("formattable")
library(formattable)
formattable(sort_new_marker,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| marker | n_unique |
|---|---|
| Other | 40 |
| None | 19 |
| Lactate dehydrogenase (LDH) | 9 |
| Alanine aminotransferase (ALT) | 2 |
| Alkaline phosphatase (ALP) | 1 |
| Aspartate aminotransferase (AST) | 1 |
| Gamma-glutamyl transferase (GGT) | 1 |
######################
# liver marker "Other"
t <- table(reconciled$`6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Comments`)
## clean the type of liver cells comments
liverMarkerComments_rows <- separate_rows(reconciled, `6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Comments`
,sep=";")
liverMarkerComments <- tibble(
study_ID = liverMarkerComments_rows$study_ID,
liverMarker = liverMarkerComments_rows$`6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Answer`,
liverMarkerComment = liverMarkerComments_rows$`6.1 Which liver markers were analysed in the presented model?_1844378b-1400-4d7a-b9f5-5538df7c7b76_Comments`
)
# remove line breaks
library(stringr)
liverMarkerComments$liverMarkerComment <- str_replace_all(liverMarkerComments$liverMarkerComment, "[\n]" , "")
liverMarkerComments_rows <- separate_rows(liverMarkerComments,liverMarkerComment
,sep=",")
liverMarkerComments_rows <- separate_rows(liverMarkerComments_rows,liverMarkerComment
,sep=" and")
# # would replace all white space
# liverMarkerComments$liverMarkerComment <- str_replace_all(liverMarkerComments$liverMarkerComment, "[^\d]+" , "")
# remove line breaks
liverMarkerComments_rows$liverMarkerComment <- str_replace_all(liverMarkerComments_rows$liverMarkerComment, "[\n]" , "")
# remove whitespace from start of str
liverMarkerComments_rows$liverMarkerComment <- str_trim(liverMarkerComments_rows$liverMarkerComment, "left")
# remove whitespace from end of str
liverMarkerComments_rows$liverMarkerComment <- str_trim(liverMarkerComments_rows$liverMarkerComment, "right")
liverMarkerComments_clean <- liverMarkerComments_rows %>%
mutate(liverMarkerComment = recode(liverMarkerComment,
"albumin" = "Albumin",
"ALB" = "Albumin",
" ALB" = "Albumin",
" ALB" = "Albumin",
"albumin ELISA" = "Albumin",
"Albumin " = "Albumin",
"albumin (ALB)" = "Albumin",
"qPCR: albumin" = "Albumin",
" Albumin" = "Albumin",
"human albumin" = "Albumin",
"urea" = "Urea",
"urea " = "Urea",
" urea" = "Urea",
" Urea" = "Urea",
" transthyre- tin (TTR)" = "TTR",
" transthyretin (TTR)" = "TTR",
" transthyretin TTR" = "TTR",
" TTR" = "TTR",
"transthyretin TTR" = "TTR",
"transthyretin (TTR)" = "TTR",
"transthyre- tin (TTR)" = "TTR",
"transferrin" = "TTR",
"HNF4a" = "HNF4alpha",
" HNF4a" = "HNF4alpha",
" HFN4alpha" = "HNF4alpha",
" HNF4alpha" = "HNF4alpha",
"HFN4A" = "HNF4alpha",
"s HNF4a" = "HNF4alpha",
" HNF4alpha" = "HNF4alpha",
" HFN4alpha" = "HNF4alpha",
"hepatocyte nuclear factor 4α (HNF4A)" = "HNF4alpha",
"HFN4alpha" = "HNF4alpha",
" AFP" = "AFP",
" alpha-fetoprotein (AFP)" = "AFP",
" alpha-fetoprotein" = "AFP",
"alpha-fetoprotein levels (HCC marker)" = "AFP",
" α-fetoprotein (AFP)" = "AFP",
"alpha-fetoprotein" = "AFP",
"Alpha-fetoprotein AFP" = "AFP",
"alpha-fetoprotein (AFP)" = "AFP",
"α-fetoprotein (AFP)" = "AFP",
" cytokeratin 19 (CK19)" = "cytokeratin 19 (CK19)",
" cytokeratin 19" = "cytokeratin 19 (CK19)",
" CK-19" = "cytokeratin 19 (CK19)",
"CK-19" = "cytokeratin 19 (CK19)",
"cytokeratin 19" = "cytokeratin 19 (CK19)",
"CK18" = "cytokeratin 18 (CK18)",
"staning for MRP2" = "MRP2",
" MRP2" = "MRP2",
"multidrug resist- ance-associated protein 2 (MRP2)" = "MRP2",
"CYP4A4 activity" = "CYP3A4",
" CYP3A4" = "CYP3A4",
" CYP3A4" = "CYP3A4",
"Cyp3A4"= "CYP3A4",
"cytochrome P450 3A4 (CYP3A4)" = "CYP3A4",
"Cytochromes 1A2" = "CYP1A2",
"alpha-1 antitrypsin (A1AT)" = "alpha-1 antitrypsin",
"alpha-1 antitrypsin (AAT)" = "alpha-1 antitrypsin",
"Alpha 1 antitrypsin" = "alpha-1 antitrypsin"
))
commentsLiver <- liverMarkerComments_clean %>% group_by(liverMarkerComment) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
commentsLiver#install.packages("formattable")
library(formattable)
formattable(commentsLiver,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| liverMarkerComment | n_unique |
|---|---|
| Albumin | 36 |
| NA | 23 |
| Urea | 16 |
| AFP | 12 |
| HNF4alpha | 9 |
| CYP3A4 | 7 |
| TTR | 5 |
| CYP1A2 | 4 |
| MRP2 | 4 |
| alpha-1 antitrypsin | 4 |
| 3 | |
| cytokeratin 18 (CK18) | 3 |
| cytokeratin 19 (CK19) | 3 |
| ASGR1 | 2 |
| CD31 | 2 |
| CYP | 2 |
| CYP2B6 | 2 |
| CYP2C19 | 2 |
| CYP2C9 | 2 |
| 2C19 | 1 |
| 3-dioxygenase | 1 |
| 3A4 | 1 |
| ABCC2 | 1 |
| ACTA2 | 1 |
| ALDOB | 1 |
| ASGPR1 | 1 |
| ATP | 1 |
| COLA1 | 1 |
| CYP1A1 | 1 |
| CYP2E1 | 1 |
| Collagen A1 | 1 |
| E-cadherin | 1 |
| Foxa3 | 1 |
| GST | 1 |
| Glutathione S-transferase alpha 1 | 1 |
| MDR1 | 1 |
| MMP2 | 1 |
| NMDA receptor 1 iso- form NR1-2 variant | 1 |
| NR1/2(PXR) | 1 |
| NR1H4(FXR) | 1 |
| OCT | 1 |
| SERPINA1 | 1 |
| TIMP1 | 1 |
| Zo-1 | 1 |
| alpha-smooth muscle actin (α-SMA) | 1 |
| asialoglycoprotein receptor 1 | 1 |
| beta-Catenin | 1 |
| bile salt export pump (BSEP) | 1 |
| ceruloplasmin | 1 |
| glucose_x0002_6-phosphatase catalytic subunit (ABCG2) | 1 |
| glutamate dehydrogenase | 1 |
| glutathione s-transferase alpha (alpha-GST) | 1 |
| hepatic markers ATP-binding cassette super-family G member 2 (G6PC) | 1 |
| hepatocyte nuclear factor 1α (HNF1A) | 1 |
| hepatocyte nuclear factor 3β (HNF3B) | 1 |
| hepatocyte nuclear factor 6 (HNF6) | 1 |
| organic anion transporter protein 1B3 (OATP1B3) | 1 |
| secretion | 1 |
| total bile acids | 1 |
| total protein | 1 |
| tryptophan 2 | 1 |
| tyrosine amino-transferase | 1 |
## trying 6.4
# which metabolites were analysed
t <- table(reconciled$`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`)
liverMetabolite <- separate_rows(reconciled, `6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`
,sep=";")
liverMetabolite$`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments` <- str_replace_all(liverMetabolite$`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`, "[\n]" , "")
liverMetabolite_comments <- liverMetabolite %>% select(c(study_ID, `6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`, `6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`))
commentsLiverMetabolite <- liverMetabolite_comments %>% group_by(`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer`) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
otherLiverMetabolite <- liverMetabolite_comments %>% group_by(`6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments`) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
# print(otherLiverMetabolite)
#install.packages("formattable")
library(formattable)
formattable(commentsLiverMetabolite,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| 6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Answer | n_unique |
|---|---|
| Albumin | 47 |
| Urea | 24 |
| None | 15 |
| Other | 7 |
| Bile acid | 3 |
#### CLEAN THIS!!
formattable(otherLiverMetabolite,
align = c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| 6.4 Which metabolites were analyzed in the study?_5652d702-1441-4234-b37d-f0b44d64c5e9_Comments | n_unique |
|---|---|
| NA | 56 |
| ASS, involved in Urea cycle | 1 |
| Alpha-fetoprotein AFP; transthyretin TTR | 1 |
| Glucose | 1 |
| albumin, alpha-fetoprotein, MKI67, Casp8 | 1 |
| alpha-fetoprotein, alpha-1-antitrypsin | 1 |
| aspartate, formate, glycine, histidine and tryptophan | 1 |
| transferrin, ceruloplasmin, alpha-1 antitrypsin | 1 |
#####################
# WHERE
table(reconciled$`6.1.1 Where were the liver markers measured?_a70225ae-f529-4b91-bcc9-8081d7efc6e2_Answer`)##
## Cells Media Media;Cells Not reported
## 10 15 19 19 # Cells Media Media;Cells Not reported
# 10 15 19 19
barplot(table(reconciled$`6.1.1 Where were the liver markers measured?_a70225ae-f529-4b91-bcc9-8081d7efc6e2_Answer`), ylab = "Number of studies", cex.names=.7, col = "yellow")
# CYP450
table(reconciled$`6.2 Does the study analyse the Cytochrome P450 CYP450 level in the print?_3c21cc1f-1431-49c7-ad8f-bda83956828a_Answer`)##
## No Yes
## 33 30 # No Yes
# 33 30
barplot(table(reconciled$`6.2 Does the study analyse the Cytochrome P450 CYP450 level in the print?_3c21cc1f-1431-49c7-ad8f-bda83956828a_Answer`), ylab = "Number of studies", cex.names=.7, col = "yellow")
table(reconciled$`6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`)##
## Cytochrome P450 1A2 (CYP1A)
## 4
## Cytochrome P450 1A2 (CYP1A);Cytochrome P450 2E1 (CYP2E)
## 1
## Cytochrome P450 1A2 (CYP1A);Cytochrome P450 3A4 (CYP3A)
## 1
## Cytochrome P450 1A2 (CYP1A);Cytochrome P450 3A4 (CYP3A);Cytochrome P450 2B6 (CYP2B)
## 1
## Cytochrome P450 1A2 (CYP1A);Cytochrome P450 3A4 (CYP3A);Cytochrome P450 2B6 (CYP2B);Cytochrome P450 2C9 (CYP2C)
## 1
## Cytochrome P450 1A2 (CYP1A);Cytochrome P450 3A4 (CYP3A);Cytochrome P450 2B6 (CYP2B);Cytochrome P450 2C9 (CYP2C);Cytochrome P450 2D6 (CYP2D)
## 2
## Cytochrome P450 1A2 (CYP1A);Cytochrome P450 3A4 (CYP3A);Cytochrome P450 2B6 (CYP2B);Cytochrome P450 2C9 (CYP2C);Cytochrome P450 2E1 (CYP2E)
## 1
## Cytochrome P450 1A2 (CYP1A);Cytochrome P450 3A4 (CYP3A);Cytochrome P450 2C9 (CYP2C);Cytochrome P450 2D6 (CYP2D)
## 1
## Cytochrome P450 1A2 (CYP1A);Cytochrome P450 3A4 (CYP3A);Other
## 3
## Cytochrome P450 2C9 (CYP2C)
## 1
## Cytochrome P450 2E1 (CYP2E)
## 1
## Cytochrome P450 3A4 (CYP3A)
## 9
## Cytochrome P450 3A4 (CYP3A);Cytochrome P450 2D6 (CYP2D)
## 1
## Cytochrome P450 3A4 (CYP3A);Cytochrome P450 2D6 (CYP2D);Cytochrome P450 2E1 (CYP2E)
## 1
## Not reported
## 2## output needs cleaning
liverCytochome <- separate_rows(reconciled, `6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`
,sep=";")
liverCytochome_only <- liverCytochome %>% select(c(study_ID, `6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`, `6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Comments`))
liverCytochome_only <- liverCytochome_only %>% group_by(`6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer`) %>% summarise(n_unique = length(unique(study_ID))) %>% arrange(desc(n_unique))
formattable(liverCytochome_only,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("yellow")
))| 6.2.1 If yes, which cytochrome isoforms were analysed?_62bdffd7-d9c7-4646-9f66-9510f5bf2649_Answer | n_unique |
|---|---|
| NA | 33 |
| Cytochrome P450 3A4 (CYP3A) | 21 |
| Cytochrome P450 1A2 (CYP1A) | 15 |
| Cytochrome P450 2C9 (CYP2C) | 6 |
| Cytochrome P450 2B6 (CYP2B) | 5 |
| Cytochrome P450 2D6 (CYP2D) | 5 |
| Cytochrome P450 2E1 (CYP2E) | 4 |
| Other | 3 |
| Not reported | 2 |
## agonists
table(reconciled$`6.3 Have agonists of the receptors for the inducibility of CYPs been applied?_ac9f38a8-8fe2-44de-a180-24fddd78fb59_Answer`)##
## No Yes
## 54 9 # No Yes
# 54 9
barplot(table(reconciled$`6.3 Have agonists of the receptors for the inducibility of CYPs been applied?_ac9f38a8-8fe2-44de-a180-24fddd78fb59_Answer`), ylab = "Number of studies", cex.names=.7, col = "yellow")
Quality
- Quality Assays
- storage conditions
- application
- time
Does the study describe quality assurance?
table(reconciled$`5.3 Does the study describe quality-assuring assays for the printed model?_09745b80-9723-4e5c-8a47-dfc8a9c71a6d_Answer`)##
## Yes
## 63# Yes
# 63Which assays were performed?
library(tidyverse)
### messy - needs cleaning
messy_assay <- table(reconciled$`5.3.1 Which assays were performed to assure the quality of the liver model?_8a415412-28c6-48ff-840a-538ea69a068f_Answer`)
# needs cleaning
assays <- separate_rows(reconciled, `5.3.1 Which assays were performed to assure the quality of the liver model?_8a415412-28c6-48ff-840a-538ea69a068f_Answer` ,sep=";")
assays_heat <- assays[,c(1, 91)]
colnames(assays_heat) <- c("study_ID", "assay")
new <- assays_heat %>% group_by(assay) %>% summarize(n_unique = length(unique(study_ID)))
new$n_unique <- as.numeric(new$n_unique)
sort_new <- new %>% arrange(desc(n_unique))
#install.packages("formattable")
library(formattable)
formattable(sort_new,
align =c("l", "r"),
list(`Indicator Name` = formatter(
"span", style = ~ style(color = "grey",font.weight = "bold")),
`n_unique`= color_bar("lightgreen")
))| assay | n_unique |
|---|---|
| Viability test | 47 |
| Histological characterization | 45 |
| Live/Dead Cell Staining | 44 |
| Enzyme linked immunosorbent Assay (ELISA) of liver markers | 30 |
| Real-time quantitative PCR of liver markers | 28 |
| Rheological test | 23 |
| Mechanical stiffness | 15 |
| Size measurement | 13 |
| Biodegradation | 4 |
| Biocompatibility | 3 |
### 10 different tests are used across 63 papers, a total of 252 tests were used.
# the most popular are listed below
## also how many assay were performed? 1, 2, 3, 4, 5, or more.
counts <- assays_heat %>% group_by(study_ID) %>% summarize(n_unique = length(unique(assay)))
table(counts$n_unique)##
## 1 2 3 4 5 6 7
## 4 10 6 17 16 8 2 # 1 2 3 4 5 6 7
# 4 10 6 17 16 8 2
hist(counts$n_unique, xlab = "Number of Assays Reported", ylab = "Number of Papers", main = "Number of Assays Peformed per Paper",)
Applications in the paper
# Application
table(reconciled$`7.1 Do the authors apply the model in the study?_a6895d38-a9ed-4605-a092-4f06ebbd9e2b_Answer`)##
## No Yes
## 35 28 # No Yes
# 35 28
barplot(table(reconciled$`7.1 Do the authors apply the model in the study?_a6895d38-a9ed-4605-a092-4f06ebbd9e2b_Answer`), ylab = "Number of studies", cex.names=.7, col = "gray")
# field of application
table(reconciled$`7.1.1 please select the field of application. _d332c2ee-9864-434f-8595-90305808e32d_Answer`)##
## Disease modeling
## 3
## Drug dosage testing
## 2
## Drug dosage testing;Disease modeling
## 1
## Drug dosage testing;Xenograft (implantation into animal);Disease modeling
## 1
## Implant / Medical surgery
## 1
## Other
## 3
## Toxicity testing
## 10
## Toxicity testing;Drug dosage testing
## 4
## Toxicity testing;Drug dosage testing;Other
## 1
## Xenograft (implantation into animal)
## 2